Factor analysis model evaluation through likelihood cross-validation

Medical research studies utilize survey instruments consisting of responses to multiple items combined into one or more scales. These studies can benefit from methods for evaluating those scales. Such an approach is presented for evaluating exploratory and confirmatory factor analysis models with decisions about covariance structure, including the number of factors, the factor extraction procedure, the allocation of survey items to summated scales and the extent of inter-scale dependence, made objectively using a likelihood-based form of cross-validation. This approach is demonstrated through example analyses using baseline data for three survey instruments from a clinical trial involving adolescents with type 1 diabetes.